////	YOU'RE NOT WELCOME! VIOLENCE AND SUPPORT FOR AN OPEN GRAZING BAN POLICY IN KADUNA, NIGERIA

											*Daniel Tuki*
	
	
** This study is based on survey data colleced in the Northern Nigerian State of Kaduna in 2021 as part of the Transnational Perspectives on Migration anbd Integration (TRANSMIT) research project. For more information on the project visit: https://www.projekte.hu-berlin.de/en/transmit



//		DEPENDENT VARIABLES
	
* Open grazing ban (grazeban): This measures the degree to which respondents believe an open grazing ban policy would be effective in reducing the incidence of farmer-herder conflicts. The responses are measured on an ordinal scale with 5 categories.
codebook grazeban
tab grazeban

	
	
//		EXPLANATORY VARIABLE

* Muslim affiliation (relig): This is a dummy variable that takes the value of 1 if a respondent is Muslim, and 0 if Christian. 

*Victimization (all) (vioaffected): This is a dummy variable that takes the value of 1 if a respondent reported that he/she or a household member had been directly affected by violence during the past decade (2011-2021) and 0 otherwise. 


* Victimization (Herders/Religious extremists/Bandits & robbers): These are dummy variables that take the value of 1 if a respondent has been victimised by a specific perpetrator (e.g., Herders, Religious extremists, and Bandits & robbers). For instance, in the case of the variable "Victimization (herder)," respondent who were victimized by herders are coded as 1. The Reference category, which is coded as 0, would be respondents who have not been victimized by herders plus those who have been victimised by other perpetrators besides herders. The same applies to the variables "Victimization (Bandits)" and "Victimization (extremists)".  

* The victimization variables were derived from the variable "vioperpetrators". This variable is based on a question where respondents were asked to specify the perpetrators of the violent incident that had afferced during the pzst decade (i.e. from 2011-2021). They were allowed to choose more than one perpetrator. The variable is in string format, and the following nominal values are used to denote the different perpetrators: 0 = Army, 1 = Police; 2 = Herders; 3 = Farmers; 4 = Cattle rustlers; 5 = Religious extremists; 6 = Bandits/robbers; 7 = Family member; 8 = Others. In the regression model, I focused on the three main perpetrators of violence (i.e., Herders, Religious extremists, and Bandits/robbers). The codes below were used to develop the dummy variables for each of the three main perpetrators. 

gen herders = strmatch(vioperpetrators, "*2*")
gen extremists = strmatch(vioperpetrators, "*5*")
gen bandits = strmatch(vioperpetrators, "*6*")

*(Other perpetrators of violence)
gen rustlers = strmatch(vioperpetrators, "*4*")
gen army = strmatch(vioperpetrators, "*0*")
gen farmer = strmatch(vioperpetrators, "*3*")
gen police = strmatch(vioperpetrators, "*1*")
gen family = strmatch(vioperpetrators, "*7*")
gen others = strmatch(vioperpetrators, "*8*")

*I derived Table 1 entitled, "Distribution of victimized respondents based on religious affiliation and perpetrators" using the above codes. 
	
	
	
//		CONTROL VARIABLES

* Married (married): This is a dummy variable that takes the value of 1 if a respondent is married or has ever been married, and 0 otherwise. It is a reduced form of the "marstat" variable.

* Gender (gender1): This is a dummy variable that takes the value of 1 if the respondent is female and 0 if male. 

* Muslim afiliation (relig): This is a dummy variable that takes the value of 1 if the respondent is muslim and 0 if Christian. 

* Age (age1): This is measured in years. 

* Conflict exposure (fp_all_20km): This measures the total number of conflict incidents involving nomadic Fulani pastorlaists within the 20 km buffer around the respondents' dwellings. It was derived from the ACLED database (Raleigh et al. 2010). To access the raw ACLED data visit: https://acleddata.com/ 
 

	
//		*DESCRIPTIVE VARIABLES*
	
* These are variables that were mentioned descriptively in the paper:

* Awareness of farmer-herder conflicts (confarm): This is a dummy variable that takes the value of 1 if the respondent is aware of farmer-herder conflicts, and 0 otherwise. 

* Government effectiveness (confarmgov): This is avariable that measures respondents' perceptions of the Nigerian government's effectiveness in handling farmer-herder conflicts. This variable was used to estimate a bivariate regression model reported in Table A2 in the appendix, and to construct Figures A1 and A2, also in the appendix. 



//		*FIXED EFFECTS*

* These are the variables from which I derived the fixed effects that were included in the regression models. They are descrived below:

*Ethnic group (ethnic): This indicates the ethnic group to which the respondents belong.
tab ethnic

*Local Government Area (lga_name): This is the name of the local governbment area (i.e. municipality) where the respondents reside.
tab lga_name

*Local Government Area identification number (lga_indicator): This is a unique identifiction number assigned to the respective local government areas. 
tab lga_indicator


********************************************************************************

//					REGRESSION RESULTS

//	Table 2: Ordered logit models regressing support for an open grazing ban policy on victimization  
	
* Model 1: Baseline - only victimization
ologit grazeban vioaffected, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 2: Baseline - only religious affiliation
ologit grazeban relig, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic	

* Model 3: Including both explanatoy variables 
ologit grazeban vioaffected relig, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 4: Adding control variavles 
ologit grazeban vioaffected relig age1 gender1 married fp_all_20km, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 5: Adding fixed effects for ethnicity & LGA (i.e., municipality)
ologit grazeban vioaffected relig age1 gender1 married fp_all_20km i.ethnic i.lga_indicator, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 6: Using only the Muslim subsample of respondents
ologit grazeban vioaffected age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
* To obtain the AIC statistics
estat ic

* Model 7: Using only the Christian subsample of respondents
ologit grazeban vioaffected age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0
* To obtain the AIC statistics
estat ic



//	Table 3: Ordered logit models regressing support for an open grazing ban policy on victimization by herders
	
* Model 1: Baseline - only herders
ologit grazeban herders, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic
	
* Model 2: Including religious affiliation 
ologit grazeban herders relig, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 3: Adding control variavles 
ologit grazeban herders relig age1 gender1 married fp_all_20km, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

* Model 4: Adding fixed effects for ethnicity & LGA (i.e., municipality)
ologit grazeban herders relig age1 gender1 married fp_all_20km i.ethnic i.lga_indicator, cluster(lga_indicator)
* To obtain the AIC statistics
estat ic

*Model 5: Using only the Muslim subsample of respondents
ologit grazeban herders age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
*To obtain the AIC statistics
estat ic

*Model 6: Using only the Christian subsample of respondents
ologit grazeban herders age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0
*To obtain the AIC statistics
estat ic

	
	
// Figure 5: Average marginal effects of religion and victimization by herders on support for an open grazing ban policy

* Panel A (Model 3)
ologit grazeban herders relig age1 gender1 married fp_all_20km i.ethnic i.lga_indicator, cluster(lga_indicator)
* Panel A: To obtain the marginal effects for religion
margins, dydx(relig)
* To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (relig, replace)
	
* Panel B: To obtain the marginal effects for victimization by herders
margins, dydx(herders)
* To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (herders_popn, replace)

// Panel C (model 5) Muslim subsample - herders
ologit grazeban relig herders age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
* Panel C: To obtain the marginal effects for victimization by herders
margins, dydx(herders)
* To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (herders_muslim, replace)

// Panel D (model 6) Christian subsample - herders
ologit grazeban relig herders age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0
* Panel D: To obtain the marginal effects for victimization by herders
margins, dydx(herders)
* To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (herders_xtian, replace)

// To combine the four graphs into a single figure
graph combine relig herders_popn herders_muslim herders_xtian
	
	
	
//	Table 4: Ordered logit models regressing support for an open grazing ban policy on victimization by religious extremists and bandits/robbers 
				
				* Religious extremists*
				
*Model 1: Full sample
ologit grazeban extremists relig age1 gender1 married fp_all_20km i.ethnic i.lga_indicator, cluster(lga_indicator)
*To obtain the AIC statistics
estat ic

*Model 2: Using the Muslim subsample
ologit grazeban extremists age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
*To obtain the AIC statistics
estat ic

*Model 3: Using the Christian subsample
*First reload the full data and run the initial set of codes
ologit grazeban extremists age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0 
*To obtain the AIC statistics
estat ic

			* Bandits/robbers*
			
*Model 4: Full sample
ologit grazeban bandits relig age1 gender1 married fp_all_20km i.ethnic i.lga_indicator, cluster(lga_indicator)
*To obtain the AIC statistics
estat ic

*Model 5: Using the Muslim subsample
ologit grazeban bandits age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
*To obtain the AIC statistics
estat ic

*Model 6: Using the Christian subsample
*First reload the full data and run the initial set of codes
ologit grazeban bandits age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0 	
*To obtain the AIC statistics
estat ic
	


// Figure 6: Average marginal effects of victimization by bandits/robbers on support for an open grazing ban policy

// Panel A (model 5): Muslim subsample - bandits
ologit grazeban bandits age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 1
*To obtain the marginal effects for victimization by bandits
margins, dydx(bandits)
*To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (bandits_muslim, replace)
	
	
// Panel B (model 6): (Christian subsample - bandits)
ologit grazeban bandits age1 gender1 married fp_all_20km i.lga_indicator, cluster(lga_indicator), if relig == 0
*To obtain the marginal effects for victimization by bandits
margins, dydx(bandits)
*To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (bandits_xtian, replace)	

// To combine the two graphs into a single figure
graph combine bandits_muslim bandits_xtian
	
	
	
********************************************************************************	
	
**					APPENDIX
	
//	*Table A1: Summary statistics

summ grazeban vioaffected herders extremists bandits relig confarmgov age1 gender1 married fp_all_20km if confarm == 1
	
	
// Table A2: Ordered logit models regressing government effectiveness on religious affiliation and victimization by herders

*Model 1: Muslim affiliation
ologit confarmgov relig i.ethnic i.lga_indicator, cluster(lga_indicator)
estat ic

*Model 2: Victimization by herders
ologit confarmgov herder i.ethnic i.lga_indicator, cluster(lga_indicator)
estat ic

*Model 3: Adding both variables
ologit confarmgov relig herder i.ethnic i.lga_indicator, cluster(lga_indicator)
estat ic



// Figure A1: Average marginal effects of religion and victimization by herders on perceptions of government effectiveness in handling farmer-herder conflicts

* Panel A (model 1): To obtain the marginal effects for model 1
ologit confarmgov relig i.ethnic i.lga_indicator, cluster(lga_indicator)
* to obtain marginal effects
margins, dydx(relig)
*To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (relig_gov_effect, replace)	

* Panel B (model 2): To obtain the marginal effects for model 2
ologit confarmgov herder i.ethnic i.lga_indicator, cluster(lga_indicator)
* To obtain the marginal effects
margins, dydx(herders)
*To plot the marginal effects as a bar chart with confidence intervals
marginsplot, recast(bar) yline(0) name (herders_gov_effect, replace)	
	
// To combine the two graphs into a single figure
graph combine relig_gov_effect herders_gov_effect
	
	
	
	
/*

 		**CODES USED TO FILTER OUT CONFLICTS INVOLVING NOMADIC FULANI HERDERS FROM THE ACLED DATASET** 
		

* To filter out incidents with the word "Pastoralist" in Associated Actors 1 & 2: 
gen pastoral_11 = strmatch(assoc_actor_1, "*Pastoralist*")
gen pastoral_22 = strmatch(assoc_actor_2, "*Pastoralist*")
*The word "Pastoralist" did not occur in the variables "actor1" and "actor2", thus the focus on only the associated actors. 

*To filter out incidents with the word "Fulani" in Actors 1 & 2, and Associated Actors 1 & 2: 
gen fulani_1 = strmatch(actor1, "*Fulani*")
gen fulani_11 = strmatch(assoc_actor_1, "*Fulani*")

gen fulani_2 = strmatch(actor2, "*Fulani*")
gen fulani_22 = strmatch(assoc_actor_2, "*Fulani*")

*key_word: To generate the variable "key_word" where observations greater than 1 imply that the event involves at least one actor who is defined as "Fulani" or "pastoralist": 
gen key_word = pastoral_11 + pastoral_22 + fulani_1 + fulani_11 + fulani_2 + fulani_22

*To drop the observations that do not contain any of the keywords: 
drop if key_word == 0 

*This leaves the subsample of observations used to contruct the variable measuring exposure to conflicts involving herders.

tab year
tab admin1
tab admin2 if admin1 == "Kaduna" & year < 2022

* In the ACLED datase, the terms "Fulani" and "pastoralists" are similar. 
	